{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任务2：\n",
    "输入1（cycle）3（EMG_lat_hams_l=emg_lh_l），输出5（muscleForce_blifemsh=mf_bm_l）\n",
    "\n",
    "似乎把3做整体输入时，cycle可以省略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from visdom import Visdom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import torch.nn.init as init\n",
    "from torchsummary import summary\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import datetime\n",
    "#import tool\n",
    "from tool import *\n",
    "import torch.nn.functional as F\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n"
     ]
    }
   ],
   "source": [
    "# 可视化\n",
    "timeForSave = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n",
    "#path = 'G:\\科研学习\\肌电信号\\Code\\musle force\\\\'\n",
    "path ='/mnt/data/ZP_Project/Muscle/muscle/'\n",
    "Visdom_Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Visdom\\\\'\n",
    "if not os.path.exists(Visdom_Dir):\n",
    "    os.makedirs(Visdom_Dir)\n",
    "image_Dir =path+'image_Mission2_FC_all_together'\n",
    "if not os.path.exists(image_Dir):\n",
    "        os.makedirs(image_Dir)\n",
    "# image_Dir ='G:\\科研学习\\肌电信号\\Code\\musle force\\image_Mission3_FC\\\\'\n",
    "# if not os.path.exists(image_Dir):\n",
    "#         os.makedirs(image_Dir)\n",
    "\n",
    "class visdom_account:\n",
    "    def __init__(self):\n",
    "        self.port = 8097\n",
    "        self.server = \"http://localhost\"\n",
    "        self.base_url = \"/\"\n",
    "        self.username = \"admin\"\n",
    "        self.passward = \"1234\"\n",
    "        self.evns = \"Muscle\"\n",
    "\n",
    "\n",
    "viz_acnt = visdom_account()\n",
    "\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz.delete_env('Muscle')  # 删除环境\n",
    "\n",
    "# viz.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#          opts={'title': 'ProcessMonitor', },)\n",
    "# viz1.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#           opts={'title': 'ProcessMonitor', },)\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz1 = Visdom(env=viz_acnt.evns,\n",
    "              log_to_filename=Visdom_Dir+'vislog_'+timeForSave)\n",
    "viz_batch = []\n",
    "for i in range(32):\n",
    "    viz_batch.append(Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed_redacted_GIL03_XSlow1.mat\n",
      "Transformed_redacted_GIL12_Fast2.mat\n",
      "Transformed_redacted_GIL02_Free2.mat\n",
      "Transformed_redacted_GIL12_slow4.mat\n",
      "Transformed_redacted_GIL04_slow3.mat\n",
      "Transformed_redacted_GIL04_Fast2.mat\n",
      "Transformed_redacted_GIL02_Fast3.mat\n",
      "Transformed_redacted_GIL04_Free3.mat\n",
      "Transformed_redacted_GIL06_slow2.mat\n",
      "Transformed_redacted_GIL11_Fast2.mat\n",
      "Transformed_redacted_GIL08_Fast1.mat\n",
      "Transformed_redacted_GIL03_Free4.mat\n",
      "Transformed_redacted_GIL06_Fast3.mat\n",
      "Transformed_redacted_GIL08_slow4.mat\n",
      "Transformed_redacted_GIL03_xFast2.mat\n",
      "Transformed_redacted_GIL01_Fast5.mat\n",
      "Transformed_redacted_GIL11_XSlow1.mat\n",
      "Transformed_redacted_GIL08_XSlow2.mat\n",
      "Transformed_redacted_GIL06_XSlow1.mat\n",
      "Transformed_redacted_GIL11_Free1.mat\n",
      "Transformed_redacted_GIL12_Free4.mat\n",
      "Transformed_redacted_GIL01_XSlow2.mat\n",
      "Transformed_redacted_GIL03_slow3.mat\n",
      "Transformed_redacted_GIL11_slow2.mat\n",
      "Transformed_redacted_GIL06_Free2.mat\n",
      "Transformed_redacted_GIL01_Free4.mat\n",
      "Transformed_redacted_GIL01_slow3.mat\n",
      "Transformed_redacted_GIL04_XSlow1.mat\n",
      "Transformed_redacted_GIL02_slow1.mat\n",
      "Transformed_redacted_GIL08_Free1.mat\n",
      "Transformed_redacted_GIL02_XSlow2.mat\n",
      "Transformed_redacted_GIL12_XSlow1.mat\n"
     ]
    }
   ],
   "source": [
    "#读取并合并\n",
    "#尝试直接把数据相加\n",
    "Dir = 'Transed_Data'\n",
    "filenames = os.listdir(path+Dir)\n",
    "# Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data\\\\'\n",
    "# filenames = os.listdir('G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data')\n",
    "\n",
    "data_emg = []\n",
    "data_mf = []\n",
    "#data_kal = []\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = scio.loadmat(path+Dir +'//'+ str(filename))\n",
    "    #print(data.keys())  #dict_keys(['__header__', '__version__', '__globals__', 'cyc', 'emg_lh_l', 'emg_rf_l', 'ka_l', 'mf_bm_l', 'mf_rf_l'])\n",
    "    data1 = data['emg_lh_l']\n",
    "    #data1 = data1.squeeze()\n",
    "    data2 = data['mf_bm_l']\n",
    "    #data3 =  data['ka_l']\n",
    "    #data2 = data2.squeeze()\n",
    "    data_emg.append(data1)\n",
    "    data_mf.append(data2)\n",
    "    #data_kal.append(data3)\n",
    "\n",
    "data_emg = np.array(data_emg)\n",
    "data_mf = np.array(data_mf)\n",
    "#data_kal = np.array(data_kal)\n",
    "set=MuscleData1(data_emg,data_mf)\n",
    "train_set,test_set = torch.utils.data.random_split(set, [28, 4])\n",
    "    # sampel = next(iter(set))\n",
    "    # print(sampel,'\\nnnnnnn')\n",
    "\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(set,[28,4])\n",
    "\n",
    "# train_loader =torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, pin_memory=False)\n",
    "# test_loader =torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, pin_memory=False)\n",
    "# train_test_loader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"此代码块为Net训练\"\"\"\n",
    "\n",
    "Net = FC_Net()\n",
    "Net.apply(weights_init)\n",
    "Net.to(device)\n",
    "\n",
    "train_loader=torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, pin_memory=False)\n",
    "test_loader=torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=False)\n",
    "\n",
    "\n",
    "criterion = torch.nn.MSELoss()\n",
    "epoch_num = 3000\n",
    "\n",
    "optimizer = torch.optim.SGD(Net.parameters(),\n",
    "lr=0.0000015,momentum=0.9)\n",
    "vizx = 0\n",
    "#开始训练\n",
    "for epoch in range(epoch_num):\n",
    "        #n = 0   #此n用于visdom画图。每个epoch对每个样本进行画图\n",
    "        for batch in train_loader:\n",
    "                vizx+=1\n",
    "                data,label = batch\n",
    "                data = torch.as_tensor(data)\n",
    "                #data2 = torch.as_tensor(data2)\n",
    "                data = data.type(torch.FloatTensor)\n",
    "                #data2 = data2.type(torch.FloatTensor)\n",
    "                \n",
    "                #print(data.shape)       #torch.Size([1, 101, 1])\n",
    "                label =torch.as_tensor(label)\n",
    "                label = label.squeeze(2)\n",
    "                #print(label.shape)\n",
    "                label = label.type(torch.FloatTensor)\n",
    "                data = data.cuda()\n",
    "                #data2 = data2.cuda()\n",
    "                label = label.cuda()\n",
    "                #print(data.shape)\n",
    "                pred = Net(data)\n",
    "                #print(pred.shape)\n",
    "                #print(label.shape)\n",
    "                #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                #print('pred',pred.shape)\n",
    "                #print('label',label.shape)     #label torch.Size([101, 1])\n",
    "                loss = criterion(pred,label)\n",
    "                \n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                #viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\n",
    "                viz_batch[0].line([float(loss)],[vizx],win='loss'+str(0), name='loss'+str(0), update='append',opts=dict(title='train_loss'))\n",
    "                optimizer.step()\n",
    "                #n +=1\n",
    "                #print(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i =0\n",
    "#测试\n",
    "for batch in test_loader:\n",
    "        data1,label = batch\n",
    "        data1 = torch.as_tensor(data1)\n",
    "        #data2 = torch.as_tensor(data2)\n",
    "        data1 = data1.type(torch.FloatTensor)\n",
    "        #data2 = data2.type(torch.FloatTensor)\n",
    "        data1 = data1.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        #data2 = data2.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        # label =label.to(device)\n",
    "        #data1 = data1.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "        pred = Net(data1)\n",
    "        #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "        pred = pred.cpu()\n",
    "        pred = pred.data.numpy()\n",
    "        pred=np.squeeze(pred)\n",
    "        label=np.squeeze(label)\n",
    "        data1 =data1.cpu()\n",
    "        #data2 =data2.cpu()\n",
    "        data1 = np.squeeze(data1)\n",
    "        #data2 = np.squeeze(data2)\n",
    "        #利用plot画图\n",
    "        plt.figure(i)\n",
    "        plt.title('test_sample'+str(i))\n",
    "        plt.plot(pred, label='pred')\n",
    "        plt.plot(label, color='red', label='GroundTruth')\n",
    "        plt.plot(data1*100, color='black', label='data1')\n",
    "        #plt.plot(data2*100, color='black', label='data2')\n",
    "        plt.legend()\n",
    "        #增\n",
    "        figname ='./image_Mission2_FC_all_together/imag_ {}.jpg'.format(i)\n",
    "        plt.savefig(figname)\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "        i +=1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "Connection to remote host was lost.\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "[Errno 111] Connection refused\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n",
      "on_close() takes 1 positional argument but 3 were given\n"
     ]
    }
   ],
   "source": [
    "# #用于打印网络里权重。默认注释掉\n",
    "# Net.state_dict().keys()\n",
    "# for i in range(len(test_loader)):\n",
    "#     print(i)\n",
    "#     # print(Net[i].fc1.weight)\n",
    "#     # print(Net[i].fc2.weight)\n",
    "#     # print(Net[i].out.weight)\n",
    "#     w1 =Net[i].fc1.weight.cpu().data.numpy()\n",
    "#     w2 =Net[i].fc2.weight.cpu().data.numpy()\n",
    "#     w3 =Net[i].out.weight.cpu().data.numpy()\n",
    "#     w1 = np.squeeze(w1)\n",
    "#     w2 = np.squeeze(w1)\n",
    "#     w3 = np.squeeze(w1)\n",
    "#     #ss\n",
    "#     plt.figure(i)\n",
    "#     plt.title('Net'+str(i))\n",
    "#     plt.plot(w1, label='fc1.weight')\n",
    "#     plt.plot(w2, color='red', label='fc2.weight')\n",
    "#     plt.plot(w3, color='black', label='out.weight')\n",
    "#     plt.legend()\n",
    "#     #figname ='./image_Mission1_FC/imag_ {}.jpg'.format(i)\n",
    "#     #plt.savefig(figname)\n",
    "#     plt.show()\n",
    "#     plt.close()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e3b95d8d6e207246d09e19daee9bbc5bbe4502ff924268508716ea3969933742"
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  "kernelspec": {
   "display_name": "Python 3.7.11 ('pytorch')",
   "language": "python",
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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